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Learning Incoherent Dictionaries for Sparse Approximation Using Iterative Projections and Rotations

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2 Author(s)
Barchiesi, D. ; School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK ; Plumbley, M.D.

This article deals with learning dictionaries for sparse approximation whose atoms are both adapted to a training set of signals and mutually incoherent. To meet this objective, we employ a dictionary learning scheme consisting of sparse approximation followed by dictionary update and we add to the latter a decorrelation step in order to reach a target mutual coherence level. This step is accomplished by an iterative projection method complemented by a rotation of the dictionary. Experiments on musical audio data and a comparison with the method of optimal coherence-constrained directions (mocod) and the incoherent k-svd (ink-svd) illustrate that the proposed algorithm can learn dictionaries that exhibit a low mutual coherence while providing a sparse approximation with better signal-to-noise ratio (snr) than the benchmark techniques.

Published in:

Signal Processing, IEEE Transactions on  (Volume:61 ,  Issue: 8 )

Date of Publication:

April15, 2013

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